Publications by authors named "D J Barnette"

Introduction: In 2015, the FDA released a Drug Safety Communication regarding a possible link between opioid exposure during early pregnancy and an increased risk of fetal neural tube defects (NTDs). At the time, the indications for opioid use during pregnancy were not changed due to incomplete maternal toxicity data and limitations in human and animal studies. To assess these knowledge gaps, largescale animal studies are ongoing; however, state-of-the-art technologies have emerged as promising tools to assess otherwise non-standard endpoints.

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Drug-induced liver injury (DILI) is a significant public health issue, but standard animal tests and clinical trials sometimes fail to predict DILI due to species differences and the relatively low number of human subjects involved in preapproval studies of a new drug, respectively. In vitro models have long been used to aid DILI prediction, with primary human hepatocytes (PHHs) being generally considered the gold standard. However, despite many efforts and decades of work, traditional culture methods have been unsuccessful in either fully preserving essential liver functions after isolation of PHHs or in emulating interactions between PHHs and hepatic nonparenchymal cells (NPCs), both of which are essential for the development of DILI under in vivo conditions.

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Coumadin (R/S-warfarin) anticoagulant therapy is highly efficacious in preventing the formation of blood clots; however, significant inter-individual variations in response risks over or under dosing resulting in adverse bleeding events or ineffective therapy, respectively. Levels of pharmacologically active forms of the drug and metabolites depend on a diversity of metabolic pathways. Cytochromes P450 play a major role in oxidizing R- and S-warfarin to 6-, 7-, 8-, 10-, and 4'-hydroxywarfarin, and warfarin alcohols form through a minor metabolic pathway involving reduction at the C11 position.

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Neurological disease and disorders remain a large public health threat. Thus, research to improve early detection and/or develop more effective treatment approaches are necessary. Although there are many common techniques and imaging modalities utilized to study these diseases, existing approaches often require a label which can be costly and time consuming.

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Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of electronic health records (EHRs) offers a unique opportunity to build machine learning algorithms to identify drug-drug interactions that drive adverse events. In this study, we investigated hospitalizations' data to study drug interactions with non-steroidal anti-inflammatory drugs (NSAIDS) that result in drug-induced liver injury (DILI).

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